Impulse Radio Research Articles

Dual-Mode Embedded Impulse-Radio Ultra-Wideband Radar System for Biomedical Applications.

This paper presents a real-time and non-contact dual-mode embedded impulse-radio (IR) ultra-wideband (UWB) radar system designed for microwave imaging and vital sign applications. The system is fully customized and composed of three main components, an RF front-end transmission block, an analog signal processing (ASP) block, and a digital processing block, which are integrated in an embedded system. The ASP block enables dual-path receiving for image construction and vital sign detection, while the digital part deals with the inverse scattering and direct current (DC) offset issues. The self-calibration technique is also incorporated into the algorithm to adjust the DC level of each antenna for DC offset compensation. The experimental results demonstrate that the IR-UWB radar, based on the proposed algorithm, successfully detected the 2D image profile of the object as confirmed by numerical derivation. In addition, the radar can wirelessly monitor vital sign behavior such as respiration and heartbeat information.

Design of a multi-standard IR-UWB emitter in a 28 nm FD-SOI technology based on the frequency transposition pulse synthesis

Ultra-WideBand Impulse Radio (IR-UWB) is a wireless communication technology well-suited for short range communications (about 10 meters) and has the advantage to allow real-time location with a precision of 5 cm. However, its use has been standardized in some standards and especially in the IEEE 802.15.4 dedicated to Wireless Personnal Area Network (WPAN) and in the IEEE 802.15.6 dedicated to Wireless Body Area Network (WBAN). After presenting requirements of each standard and a literature survey on the IR-UWB emitter, the design of a multi-standard IR-emitter based on the frequency transposition pulse synthesis is proposed in a 28 nm FD-SOI technology. The proposed design has been validated at the transistor level using electrical simulations which take into account the parasitic capacitance at each critical node of the circuit. Moreover, results show that the proposed circuit has the potential to be the first multi-standard IR-UWB emitter which covers every channel of the IEEE 802.15.4 and 802.15.6 standards.

Design and Implementation of a Transmitter for IR-UWB Standard

In wireless communication, the Ultrawide Band (UWB) is a technique for achieving a higher data rate, low power consumption, and less complexity. Impulse Radio - Ultra-Wide Band (IR-UWB) uses the baseband signal technique, reducing circuit complexity and power consumption. This work proposes an IR-UWB transmitter block with low power and tunable bandwidth that meets the UWB regulations of LRP (Low-Rate Pulse) UWB. The transmitter proposed uses a time-interleaved architecture using 0.18um CMOS technology using Cadence Virtuoso, which consists of On Off Keying (OOK) Circuit for the four stages of a pulse generator that operates in 6.17GHz-9.10GHz. The width of the pulse is between 0.096ns to 0.162ns using a current steering inverter. The proposed transmitter block has an output swing of 30 mV at 1MHz input Pulse Repetition Frequency (PRF) and a maximum PRF of 1GHz, at a 1GHz pulse rate, the power consumption was measured to be 8.3 mW.

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Developing accurate classification models for radar-based Human Activity Recognition (HAR), capable of solving real-world problems, depends heavily on the amount of available data. In this paper, we propose a simple, effective, and generalizable data augmentation strategy along with preprocessing for micro-Doppler signatures to enhance recognition performance. By leveraging the decomposition properties of the Discrete Wavelet Transform (DWT), new samples are generated with distinct characteristics that do not overlap with those of the original samples. The micro-Doppler signatures are projected onto the DWT space for the decomposition process using the Haar wavelet. The returned decomposition components are used in different configurations to generate new data. Three new samples are obtained from a single spectrogram, which increases the amount of training data without creating duplicates. Next, the augmented samples are processed using the Sobel filter. This step allows each sample to be expanded into three representations, including the gradient in the x-direction (Dx), y-direction (Dy), and both x- and y-directions (Dxy). These representations are used as input for training a three-input convolutional neural network-long short-term memory support vector machine (CNN-LSTM-SVM) model. We have assessed the feasibility of our solution by evaluating it on three datasets containing micro-Doppler signatures of human activities, including Frequency Modulated Continuous Wave (FMCW) 77 GHz, FMCW 24 GHz, and Impulse Radio Ultra-Wide Band (IR-UWB) 10 GHz datasets. Several experiments have been carried out to evaluate the model's performance with the inclusion of additional samples. The model was trained from scratch only on the augmented samples and tested on the original samples. Our augmentation approach has been thoroughly evaluated using various metrics, including accuracy, precision, recall, and F1-score. The results demonstrate a substantial improvement in the recognition rate and effectively alleviate the overfitting effect. Accuracies of 96.47%, 94.27%, and 98.18% are obtained for the FMCW 77 GHz, FMCW 24 GHz, and IR- UWB 10 GHz datasets, respectively. The findings of the study demonstrate the utility of DWT to enrich micro-Doppler training samples to improve HAR performance. Furthermore, the processing step was found to be efficient in enhancing the classification accuracy, achieving 96.78%, 96.32%, and 100% for the FMCW 77 GHz, FMCW 24 GHz, and IR-UWB 10 GHz datasets, respectively.

An Excess Kurtosis People Counting System Based on 1DCNN-LSTM Using Impulse Radio Ultra-Wide Band Radar Signals

As the Artificial Intelligence of Things (AIOT) and ubiquitous sensing technologies have been leaping forward, numerous scholars have placed a greater focus on the use of Impulse Radio Ultra-Wide Band (IR-UWB) radar signals for Region of Interest (ROI) population estimation. To address the problem concerning the fact that existing algorithms or models cannot accurately detect the number of people counted in ROI from low signal-to-noise ratio (SNR) received signals, an effective 1DCNN-LSTM model was proposed in this study to accurately detect the number of targets even in low-SNR environments with considerable people. First, human-induced excess kurtosis was detected by setting a threshold using the optimized CLEAN algorithm. Next, the preprocessed IR-UWB radar signal pulses were bundled into frames, and the resulting peaks were grouped to develop feature vectors. Subsequently, the sample set was trained based on the 1DCNN-LSTM algorithm neural network structure. In this study, the IR-UWB radar signal data were acquired from different real environments with different numbers of subjects (0–10). As indicated by the experimental results, the average accuracy of the proposed 1DCNN-LSTM model for the recognition of people counting reached 86.66% at ROI. In general, a high-accuracy, low-complexity, and high-robustness solution in IR-UWB radar people counting was presented in this study.

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference

Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the “semantics” of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio (IR) transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics.

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.

Neuromorphic Integrated Sensing and Communications

Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless Internet-of-Things (IoT) networks. In this letter, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and to detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metrics for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.

Design and Analysis of Impulse Radio (IR) Based UWB Transmitter with Antenna

The use of ultra-wideband (UWB) in target detection, radar and wireless connectivity, specifically in the medical world, has attracted a lot of attention. The concept that the IR-UWB system does not necessarily require carrier signals is one of its most appealing features. IR-UWB can transmit information using short Gaussian monocycle pulses. In light of these advantages, this paper proposes a novel UWB transmitter system which consists of UWB signal generating circuits and UWB antenna, which work together to create entire UWB transmitter. It is based on impulses and has a simple architecture with low power consumption. The proposed transmitter is realized in Cadence tools with 90nm CMOS technology and proposed UWB antenna is simulated using Advanced Design System (ADS) simulator software. In addition, the transmitter circuit and the antenna are co-simulated using ADS software. The illustrated UWB transmitter uses a low-power supply and generates pulse amplitude with pulse duration of for the Gaussian monocycle pulse. Due to its increased output voltage swing and reduced power consumption when comparing to other circuits, the proposed architecture is functional and suitable for use in short-range wireless networks and medical applications.

A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter with Simultaneous Inductive Powering and Data Receiving.

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that always resonates in the steady state during transmission. A new clipped-sinusoid scheme is proposed for on-off keying (OOK)-modulation, which is implemented by a voltage clipper circuit with on-chip biasing generation. The TX is designed to provide a high data-rate wireless link within the 3-5 GHz band. The chip was fabricated in 130nm CMOS technology and fully characterized. State-of-the-art power efficiency of 21.3% was achieved at a data-rate of 230Mbps and energy consumption of 21pJ/b. A bit-error-rate (BER) of less than 10 -6 was measured at a distance of 1m without pulse averaging. In addition, simultaneous wireless powering and VCO-based data transmission are supported. A potential extension to a VCO-free all-wireless mode to further reduce the power consumption is also discussed.

Small Coil Antenna With Magnetic Sheet for Implantable Medical Device Communication in 40–60 MHz Band

Low-frequency communication is one of the best ways to reduce signal attenuation from the human body, although slow data rates make stable wireless communication within the human body difficult. In this paper, we designed two coil antennas for wireless implant communication, including a small dual-band transmitting antenna that can be placed inside the human body and a corresponding receiving antenna that can be mounted on the abdomen of the human body. The proposed antenna is fed from a battery-powered impulse radio (IR) transceiver peaking at 10–60 MHz, and the antenna is designed to be wideband for higher data communication rate. Simulation and measurement of reflection and transmission coefficients confirmed operation at 40–60 MHz. In addition, a battery-powered IR transceiver is used to measure the transmission performance in a 2/3 muscle phantom. As a result, communication at a data rate of 20 Mbps is achieved at a distance of 90 mm in the 2/3 muscle phantom.

Spectrally efficient IR-UWB pulse designs based on linear combinations of Gaussian Derivatives

Pulse design is critical for impulse radio communications, as it determines the transmission efficiency with respect to regulation spectral limits. In this paper, we propose the design of several novel pulse shapes relying on combinations of Gaussian derivatives with the target of improving the spectral efficiency. A general model for maximizing the efficiency of dual and triplet couplings is presented, employing the interior point global optimization algorithm. Then, the spectrum peak frequency is derived in closed form for any combination of two Gaussians with consecutive orders of derivation. The parameters controlling the time properties of the generated waveforms have been adequately adjusted to reach the best compliance with the spectral mask. Novel pulses have been investigated providing a high efficiency using simple generation mechanisms, which can be practically implemented via analog circuits. An efficiency gain of more than 20% has been realized by our newly designed triplet combination over the conventional 5th order Gaussian derivative. Results demonstrated the advantage of the proposed pulse shapes in terms of the bit error rate performance for 2 Gbps OOK and 1 Gbps PPM in AWGN and multipath channels.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Generalized Receiver Employment in Ultra-Wideband Systems: Performance Analysis

In the present paper, a performance analysis of an ultra-wideband (UWB) system based on the ge-neralized approach to signal processing in noise is discussed. The UWB system utilizes a new pulse design that made the performance analysis possible, since the new not only has a short duration to reduce collision, but is also spectrally compliant to the standards on UWB systems. We present the performance analysis of the UWB system constructed based on the generalized receiver processing different pulse shapes and with various num-bers of users. New simulation results on multiuser performance of the impulse radio are also presented.

Positioning the Adjacent Buried Objects Using UWB Technology Combine with Levenberg Marquardt Algorithm

The determination of the buried objects and cracks in building structures is an important issue in real-life. In this paper, we propose a new method called Correlation Function Separation Technique (CFST) combine with the Lervenberg-Marquardt Algorithm (LMA) using the Impulse Radio Ultra-Wide Band (IR-UWB) penetrating system to improve the accuracy in detecting and positioning of the adjacent buried objects in building structures. Based on the UWB signal processing, the proposed method can be used to determine both the relative permittivity of the environment and the position of the buried objects, especially the adjacent objects. The analytical method is validated by mathematical proofs and Matlab simulations, and the position errors are used to assess the performance of proposed method. The numerical results shown that the proposed method can be used for positioning the adjacent buried objects in the homogeneous environment which has an average positioning error of 3.52cm, which is smaller than that of the conventional method based on B-canned radar images processing.

Reliable and Low-Power Communications System Based on IR-UWB for Offshore Wind Turbines

In this paper, we propose the design of a low-power wireless sensor network architecture that enables robust communications inside offshore wind turbines. This research work is in the scope of the WATEREYE EU Project, where we have designed a corrosion monitoring solution to work unattended. The architecture is composed of several fixed sensor nodes, one mobile sensor node, several anchors and the WATEREYE Computer (WEC). Our approach is based on Impulse Radio Ultra wideband (IR-UWB) technology offering reliable and low-power communications in these harsh environments. On top of that, we propose a double star network using two UWB channels for the following purposes: one network for communications to send the sensor data and another one for ranging estimations to calculate the indoor positioning of the mobile sensor node. The power strategies applied to our system, at Hardware (HW) and Firmware (FW) levels, are described in detail. Furthermore, we present power consumption measurements obtaining the power profiles and the autonomy of the most important components of the proposed architecture supplied by battery. On the other hand, we describe the methodology to analyze the range, reliability and continuity of the two UWB links providing the packet loss and gaps as a function of distance. The proposed communications system has been validated in three different scenarios considering two of them very hostile environments. Furthermore, one of the scenarios is a real offshore wind turbine.

A Wireless Communication Network With a Ballistocardiography Experiment on the ISS: Scenario, Components and Preflight Demonstration

This paper introduces Wireless Compose-2 (WICO2), which is an experiment for the International Space Station (ISS) in order to demonstrate the provision of a flexible and adaptable wireless network infrastructure integrated into a commodity item to conduct and execute low-power, low-weight and wireless experiments in the scientific and medical domain. Recent work revealed a great potential in utilizing Wireless Sensor Network (WSN) in space habitats; however, the focus was only placed on sensing in the narrowband Industrial Scientific and Medical (ISM) 2.45 GHz band. This work extends these capabilities by utilizing Impulse Radio Ultra Wideband (IR-UWB) for ranging and evaluates the use of internal light sources for energy harvesting to drive the sensor nodes. The focus of the WICO2 experiment is on the operation of the scientific experiment Ballistocardiography for Extraterrestrial Applications and long-Term missions (BEAT), which is a demonstration of novel Ballistocardiography (BCG) sensors to monitor important cardiovascular parameters in a microgravity environment. Integrated in a Smart-Shirt, it will form a Body Area Network and make use of the IR-UWB communication to transmit the data to the network. This paper describes the concept and pre-flight test results to demonstrate the correct function, usability and performance of WICO2.

Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.